<![CDATA[Indonesia launched its Making Indonesia 4.0 roadmap in 2018 with the explicit ambition of reversing premature deindustrialization and lifting the country out of the middle-income trap. Seven years later, the gap between policy aspiration and shop-floor reality remains stark: national readiness scores are low, adoption is concentrated in large firms, and a proliferation of pilot projects has not translated into plant-wide transformation. This commentary argues that Indonesia is caught in a “pilot trap” the root causes of which are organizational and institutional rather than purely technological. Drawing on the readiness-assessment literature (INDI 4.0), technology-adoption theory, and the evidence base on machine learning in manufacturing, this article contend that artificial intelligence will deliver value in Indonesian manufacturing only where three preconditions—data infrastructure, human capital, and credible business cases—are met, and that the dominant small- and medium-enterprise segment risks exclusion from the digital industrial transition. This article critically examine four tensions: the validity of self-reported readiness indices, automation versus employment in a labor-abundant economy, state-versus market-led diffusion, and the persistent gap between AI’s promised and captured value. This article conclude with a targeted agenda for researchers, engineers, and policymakers that reframes Industry 4.0 in Indonesia from a technology-acquisition problem to a capability-building problem.]]>
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